The
decorrelation
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> gmrt_on_paper5 gmrt_on_paper12 mean_jerk_on_paper14 mean_jerk_on_paper16 gmrt_on_paper18 mean_jerk_on_paper3
#> air_time1 disp_index1 gmrt_in_air1 gmrt_on_paper1
#> 0.43777778 0.48222222 0.49111111 0.79333333
#> max_x_extension1 max_y_extension1
#> 0.02222222 0.15111111
#>
#> Included: 450 , Uni p: 0.0003333333 , Base Size: 216 , Rcrit: 0.2555213
#>
#>
1 <R=0.999,thr=0.950>, Top: 75< 9 >[Fa= 75 ]( 75 , 131 , 0 ),<|>Tot Used: 206 , Added: 131 , Zero Std: 0 , Max Cor: 0.990
#>
2 <R=0.990,thr=0.950>, Top: 5< 3 >[Fa= 80 ]( 5 , 7 , 75 ),<|>Tot Used: 212 , Added: 7 , Zero Std: 0 , Max Cor: 0.950
#>
3 <R=0.950,thr=0.900>, Top: 39< 1 >[Fa= 104 ]( 37 , 39 , 80 ),<|>Tot Used: 260 , Added: 39 , Zero Std: 0 , Max Cor: 0.919
#>
4 <R=0.919,thr=0.900>, Top: 3< 1 >[Fa= 104 ]( 3 , 3 , 104 ),<|>Tot Used: 260 , Added: 3 , Zero Std: 0 , Max Cor: 0.899
#>
5 <R=0.899,thr=0.800>, Top: 50< 1 >[Fa= 135 ]( 48 , 60 , 104 ),<|>Tot Used: 327 , Added: 60 , Zero Std: 0 , Max Cor: 0.874
#>
6 <R=0.874,thr=0.800>, Top: 12< 1 >[Fa= 144 ]( 12 , 12 , 135 ),<|>Tot Used: 336 , Added: 12 , Zero Std: 0 , Max Cor: 0.926
#>
7 <R=0.926,thr=0.900>, Top: 1< 1 >[Fa= 144 ]( 1 , 1 , 144 ),<|>Tot Used: 336 , Added: 1 , Zero Std: 0 , Max Cor: 0.887
#>
8 <R=0.887,thr=0.800>, Top: 2< 1 >[Fa= 144 ]( 2 , 2 , 144 ),<|>Tot Used: 336 , Added: 2 , Zero Std: 0 , Max Cor: 0.799
#>
9 <R=0.799,thr=0.800>
#>
[ 9 ], 0.79919 Decor Dimension: 336 Nused: 336 . Cor to Base: 209 , ABase: 450 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
692
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
135
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
4.57
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
4.45
The decorrelation
matrix
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}

Formulas
Network
Displaying the features associations
par(op)
if (ncol(dataframe) < 1000)
{
DEdataframeB <- ILAA(dataframe,verbose=TRUE,thr=thro,bootstrap=30)
transform <- 1*(attr(DEdataframeB,"UPLTM") != 0)
print(ncol(transform))
thrcol <- 1 + 0.025*nrow(transform)
rsum <- apply(1*(transform !=0),1,sum) > 2
csum <- apply(1*(transform !=0),2,sum) > thrcol | rsum
transform <- transform[csum,csum]
csum <- (apply(1*(transform !=0),2,sum) > 1) & (apply(1*(transform !=0),1,sum) > 1)
transform <- transform[csum,csum]
print(ncol(transform))
if (ncol(transform)>100)
{
thrcol <- 1 + 0.10*nrow(transform)
rsum <- apply(1*(transform !=0),1,sum) > 4
csum <- apply(1*(transform !=0),2,sum) > thrcol | rsum
transform <- transform[csum,csum]
csum <- (apply(1*(transform !=0),2,sum) > 3) & (apply(1*(transform !=0),1,sum) > 3)
transform <- transform[csum,csum]
}
print(ncol(transform))
if (ncol(transform)>100)
{
thrcol <- 1 + 0.20*nrow(transform)
rsum <- apply(1*(transform !=0),1,sum) > 8
csum <- apply(1*(transform !=0),2,sum) > thrcol | rsum
transform <- transform[csum,csum]
csum <- (apply(1*(transform !=0),2,sum) > 7) & (apply(1*(transform !=0),1,sum) > 7)
transform <- transform[csum,csum]
}
print(ncol(transform))
if ((ncol(transform) > 10) && (ncol(transform) < 150))
{
gplots::heatmap.2(transform,
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Red Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
colnames(transform) <- str_remove_all(colnames(transform),"La_")
VertexSize <- apply(transform,2,mean)
VertexSize <- 5*VertexSize/max(VertexSize)
gr <- graph_from_adjacency_matrix(transform,mode = "directed",diag = FALSE,weighted=TRUE)
gr$layout <- layout_with_fr
fc <- cluster_optimal(gr)
plot(fc, gr,
edge.width = 0.5*E(gr)$weight,
vertex.size=VertexSize,
edge.arrow.size=0.5,
edge.arrow.width=0.5,
vertex.label.cex=0.65,
vertex.label.dist=1,
main="Feature Association")
}
}
#> fast | LM |
#> gmrt_on_paper5 gmrt_on_paper12 mean_jerk_on_paper14 mean_jerk_on_paper16 gmrt_on_paper18 mean_jerk_on_paper3
#> air_time1 disp_index1 gmrt_in_air1 gmrt_on_paper1
#> 0.43777778 0.48222222 0.49111111 0.79333333
#> max_x_extension1 max_y_extension1
#> 0.02222222 0.15111111
#>
#> Included: 450 , Uni p: 0.0003333333 , Base Size: 216 , Rcrit: 0.2555213
#>
#>
1 <R=0.999,thr=0.950>, Top: 75< 9 >[Fa= 75 ]( 75 , 131 , 0 ),<|>Tot Used: 206 , Added: 131 , Zero Std: 0 , Max Cor: 0.990
#>
2 <R=0.990,thr=0.950>, Top: 5< 3 >[Fa= 80 ]( 5 , 7 , 75 ),<|>Tot Used: 212 , Added: 7 , Zero Std: 0 , Max Cor: 0.950
#>
3 <R=0.950,thr=0.900>, Top: 39< 1 >[Fa= 104 ]( 37 , 39 , 80 ),<|>Tot Used: 260 , Added: 39 , Zero Std: 0 , Max Cor: 0.919
#>
4 <R=0.919,thr=0.900>, Top: 3< 1 >[Fa= 104 ]( 3 , 3 , 104 ),<|>Tot Used: 260 , Added: 3 , Zero Std: 0 , Max Cor: 0.899
#>
5 <R=0.899,thr=0.800>, Top: 50< 1 >[Fa= 135 ]( 48 , 60 , 104 ),<|>Tot Used: 327 , Added: 60 , Zero Std: 0 , Max Cor: 0.874
#>
6 <R=0.874,thr=0.800>, Top: 12< 1 >[Fa= 144 ]( 12 , 12 , 135 ),<|>Tot Used: 336 , Added: 12 , Zero Std: 0 , Max Cor: 0.926
#>
7 <R=0.926,thr=0.900>, Top: 1< 1 >[Fa= 144 ]( 1 , 1 , 144 ),<|>Tot Used: 336 , Added: 1 , Zero Std: 0 , Max Cor: 0.887
#>
8 <R=0.887,thr=0.800>, Top: 2< 1 >[Fa= 144 ]( 2 , 2 , 144 ),<|>Tot Used: 336 , Added: 2 , Zero Std: 0 , Max Cor: 0.799
#>
9 <R=0.799,thr=0.800>
#>
[ 9 ], 0.79919 Decor Dimension: 336 Nused: 336 . Cor to Base: 209 , ABase: 450 , Outcome Base: 0
#>
bootstrapping->..............................
#>
[1] 336
#> [1] 42
#> [1] 42
#> [1] 42


par(op)
Univariate
Analysis
Univariate
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : mean_jerk_in_air6 200 : disp_index12 300 : mean_speed_in_air17
400 : gmrt_on_paper23
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : La_mean_jerk_in_air6 200 : La_disp_index12 300 :
La_mean_speed_in_air17 400 : La_gmrt_on_paper23
Final Table
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| total_time23 |
37.2 |
0.503 |
36.7 |
0.484 |
1.03e-05 |
0.863 |
| total_time15 |
38.1 |
0.875 |
37.1 |
0.421 |
5.44e-01 |
0.844 |
| air_time23 |
36.6 |
0.626 |
35.9 |
0.656 |
6.92e-03 |
0.844 |
| air_time15 |
37.7 |
1.094 |
36.6 |
0.615 |
5.06e-01 |
0.829 |
| total_time17 |
38.5 |
0.681 |
37.8 |
0.614 |
4.00e-03 |
0.824 |
| paper_time23 |
36.4 |
0.439 |
36.0 |
0.231 |
6.72e-01 |
0.814 |
| air_time17 |
37.9 |
0.914 |
37.0 |
0.795 |
3.52e-02 |
0.806 |
| paper_time17 |
37.6 |
0.395 |
37.2 |
0.439 |
1.28e-03 |
0.796 |
| total_time6 |
37.1 |
0.777 |
36.4 |
0.447 |
7.16e-01 |
0.790 |
| air_time16 |
36.4 |
1.131 |
35.2 |
0.867 |
9.38e-01 |
0.787 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| total_time23 |
37.2307 |
0.503 |
36.666 |
0.484 |
1.03e-05 |
0.863 |
| total_time15 |
38.0918 |
0.875 |
37.146 |
0.421 |
5.44e-01 |
0.844 |
| air_time17 |
37.9116 |
0.914 |
37.000 |
0.795 |
3.52e-02 |
0.806 |
| paper_time17 |
37.6037 |
0.395 |
37.205 |
0.439 |
1.28e-03 |
0.796 |
| total_time6 |
37.1004 |
0.777 |
36.368 |
0.447 |
7.16e-01 |
0.790 |
| air_time16 |
36.3573 |
1.131 |
35.240 |
0.867 |
9.38e-01 |
0.787 |
| total_time7 |
37.1660 |
0.690 |
36.578 |
0.812 |
1.87e-03 |
0.785 |
| total_time22 |
37.2925 |
0.783 |
36.656 |
0.346 |
5.74e-01 |
0.780 |
| gmrt_in_air7 |
32.9484 |
0.405 |
33.382 |
0.396 |
9.99e-01 |
0.775 |
| total_time9 |
37.0580 |
0.769 |
36.334 |
0.482 |
7.12e-01 |
0.774 |
| La_pressure_var5 |
1.2955 |
1.270 |
0.409 |
0.837 |
4.47e-01 |
0.738 |
| La_pressure_mean2 |
0.0214 |
0.298 |
0.219 |
0.156 |
7.30e-02 |
0.737 |
| La_disp_index17 |
-35.4440 |
0.142 |
-35.557 |
0.134 |
1.61e-01 |
0.731 |
| La_gmrt_on_paper2 |
0.3429 |
0.605 |
0.821 |
0.488 |
7.04e-01 |
0.725 |
| La_paper_time23 |
57.6825 |
0.224 |
57.508 |
0.204 |
8.41e-01 |
0.723 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| total_time23 |
NA |
37.2307 |
0.503 |
36.666 |
0.484 |
1.03e-05 |
0.863 |
0.863 |
1 |
| total_time231 |
NA |
37.2307 |
0.503 |
36.666 |
0.484 |
1.03e-05 |
0.863 |
NA |
NA |
| total_time15 |
NA |
38.0918 |
0.875 |
37.146 |
0.421 |
5.44e-01 |
0.844 |
0.844 |
1 |
| total_time151 |
NA |
38.0918 |
0.875 |
37.146 |
0.421 |
5.44e-01 |
0.844 |
NA |
NA |
| air_time23 |
NA |
36.6116 |
0.626 |
35.858 |
0.656 |
6.92e-03 |
0.844 |
0.844 |
NA |
| air_time15 |
NA |
37.7203 |
1.094 |
36.607 |
0.615 |
5.06e-01 |
0.829 |
0.829 |
NA |
| total_time17 |
NA |
38.5262 |
0.681 |
37.848 |
0.614 |
4.00e-03 |
0.824 |
0.824 |
NA |
| paper_time23 |
NA |
36.4011 |
0.439 |
36.001 |
0.231 |
6.72e-01 |
0.814 |
0.814 |
NA |
| air_time17 |
NA |
37.9116 |
0.914 |
37.000 |
0.795 |
3.52e-02 |
0.806 |
0.806 |
1 |
| air_time171 |
NA |
37.9116 |
0.914 |
37.000 |
0.795 |
3.52e-02 |
0.806 |
NA |
NA |
| paper_time17 |
NA |
37.6037 |
0.395 |
37.205 |
0.439 |
1.28e-03 |
0.796 |
0.796 |
NA |
| paper_time171 |
NA |
37.6037 |
0.395 |
37.205 |
0.439 |
1.28e-03 |
0.796 |
NA |
NA |
| total_time6 |
NA |
37.1004 |
0.777 |
36.368 |
0.447 |
7.16e-01 |
0.790 |
0.790 |
1 |
| total_time61 |
NA |
37.1004 |
0.777 |
36.368 |
0.447 |
7.16e-01 |
0.790 |
NA |
NA |
| air_time16 |
NA |
36.3573 |
1.131 |
35.240 |
0.867 |
9.38e-01 |
0.787 |
0.787 |
1 |
| air_time161 |
NA |
36.3573 |
1.131 |
35.240 |
0.867 |
9.38e-01 |
0.787 |
NA |
NA |
| total_time7 |
NA |
37.1660 |
0.690 |
36.578 |
0.812 |
1.87e-03 |
0.785 |
0.785 |
1 |
| total_time22 |
NA |
37.2925 |
0.783 |
36.656 |
0.346 |
5.74e-01 |
0.780 |
0.780 |
1 |
| gmrt_in_air7 |
NA |
32.9484 |
0.405 |
33.382 |
0.396 |
9.99e-01 |
0.775 |
0.775 |
1 |
| total_time9 |
NA |
37.0580 |
0.769 |
36.334 |
0.482 |
7.12e-01 |
0.774 |
0.774 |
2 |
| La_pressure_var5 |
- (1.169)gmrt_on_paper5 + pressure_var5 |
1.2955 |
1.270 |
0.409 |
0.837 |
4.47e-01 |
0.738 |
0.274 |
-1 |
| La_pressure_mean2 |
- (0.970)max_y_extension2 +
(3.71e-03)mean_jerk_on_paper2 + pressure_mean2 |
0.0214 |
0.298 |
0.219 |
0.156 |
7.30e-02 |
0.737 |
0.312 |
-2 |
| La_disp_index17 |
+ disp_index17 - (1.415)max_y_extension17 |
-35.4440 |
0.142 |
-35.557 |
0.134 |
1.61e-01 |
0.731 |
0.736 |
-1 |
| La_gmrt_on_paper2 |
+ gmrt_on_paper2 - (1.369)mean_jerk_on_paper2 |
0.3429 |
0.605 |
0.821 |
0.488 |
7.04e-01 |
0.725 |
0.337 |
0 |
| La_paper_time23 |
+ (0.746)mean_speed_on_paper23 + paper_time23 |
57.6825 |
0.224 |
57.508 |
0.204 |
8.41e-01 |
0.723 |
0.814 |
0 |
Comparing ILAA vs
PCA vs EFA
PCA
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")

EFA
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}

Effect on CAR
modeling
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}

pander::pander(table(dataframe[,outcome],pr))
pander::pander(ptab$detail[c(5,3,4,6),])
| 5 |
diag.ac |
0.920 |
0.869 |
0.955 |
| 3 |
se |
0.921 |
0.845 |
0.968 |
| 4 |
sp |
0.918 |
0.838 |
0.966 |
| 6 |
diag.or |
130.531 |
43.775 |
389.223 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="ILAA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}

pander::pander(table(DEdataframe[,outcome],pr))
pander::pander(ptab$detail[c(5,3,4,6),])
| 5 |
diag.ac |
0.908 |
0.855 |
0.947 |
| 3 |
se |
0.944 |
0.874 |
0.982 |
| 4 |
sp |
0.871 |
0.780 |
0.934 |
| 6 |
diag.or |
113.018 |
37.532 |
340.322 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}

pander::pander(table(PCAdataframe[,outcome],pr))
pander::pander(ptab$detail[c(5,3,4,6),])
| 5 |
diag.ac |
0.874 |
0.815 |
0.919 |
| 3 |
se |
0.978 |
0.921 |
0.997 |
| 4 |
sp |
0.765 |
0.660 |
0.850 |
| 6 |
diag.or |
141.375 |
31.905 |
626.443 |
par(op)
EFA
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}

pander::pander(table(EFAdataframe[,outcome],pr))
pander::pander(ptab$detail[c(5,3,4,6),])
| 5 |
diag.ac |
0.833 |
0.769 |
0.885 |
| 3 |
se |
0.753 |
0.650 |
0.838 |
| 4 |
sp |
0.918 |
0.838 |
0.966 |
| 6 |
diag.or |
33.935 |
13.646 |
84.393 |
par(op)